Hybrid Handwriting Character Recognition with Transfer Deep Learning

Handwriting character recognition is useful and important in terms of allowing correct recognition and interpretation of all characters such as handwritten letters, numbers and figures. Deep convolutional neural networks have been used frequently for computer vision in recent years due to high performance in image processing, feature extraction and classification. A lot of sample data, processing power and time are needed to train CNNs. Transfer learning enables us to obtain specific CNNs for the classes we want by minimizing these needs In this work, firstly, different CNN models are trained with transfer learning by using NIST19 dataset with handwritten characters, and then a hybrid model is created by evaluating the results of each CNN and revealing the best value. As a result of the experiment carried out on the test data set, it is observed that a performance increase of 1.1% is achieved with the created model.